phydev

from typing import Type, Dict
import numpy as np
import pandas as pd

def bootstrap(data: Type[pd.DataFrame], 
              n_resamples: int, 
              id_column: str, 
              seed: int=42) -> Type[pd.DataFrame]:
    """
    Draw N resamples with replacements from the given data.

phydev

# simulation of models based on gene expression data
# we create two different datasets drawn from normal distributions
# with different mean and standard deviation
# we then normalize the data using quantile normalization
# and fit a logistic regression model with glmnet

# load libraries
library(dplyr)
library(glmnet)
library(preprocessCore)

phydev

def sample_and_shuffle(file, frac=0.1, save_to_file = False, **kwargs):
    """
    shuffle each column in a given dataframe and draw n samples
    this is useful for developing methods out of the server for sensitive data
    and testing on a data file that keeps the same structure

    :param file: path to csv file
    :param frac: fraction of data to sample
    :param save_to_file: save the sampled data to a csv file
    :param kwargs: keyword arguments for pandas.read_csv

phydev

import numpy as np

def brownian_particle(n_steps, n_samples, dx, y0):
    """
    Brownian particle simulator
    
    :param n_steps: total steps
    :param n_samples: number of trajectories
    :param dx: maximum step length
    :param y0: starting position

phydev

Singularity

Debian installation

1. Access the following helper to add the neurodebian repository in your apt list https://neuro.debian.net/install_pkg.html?p=fsl

I'm using Debian GNU/Linux 11.0 (bullseye) and a server in Munich.

phydev

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d, Axes3D
from dolfin import *
from fenics import *
from matplotlib import cm


# model parameters
γ_GFR = 1.  # rate of GFR reaching its steady state

phydev

"""
In this script we integrate the diffusion equation by applying the stencil matrix through
the convolve function from scipy.ndimage. This function applies a convolution, which means 
that for each element in matrix phi it will multiply the weights in the intencil to the 
element itself and its 8 nearest neighbors, summing the result subsequently. 
"""

import numpy as np
import matplotlib.pyplot as plt
from scipy.ndimage import convolve

phydev

import numpy as np
import matplotlib.pyplot as plt

def xdot(x, y, alpha, beta, gamma, delta):
    """
    this function represents the rate of change of the prey population
    """
    return alpha * x  - beta * x * y

def ydot(x, y, alpha, beta, gamma, delta):

phydev

library(tidyverse)
library(googleLanguageR)

# STEP1: Generate access key for google cloud 
# read more: https://cloud.google.com/translate/docs/basic/setup-basic

gl_auth('auth_key.json') # access Google Cloud Translation API with your credentials

df <- read.csv('german_data_dictionary.csv') # read data

phydev

"""
    LASSO regression (L1 regularization) with gradient descent
    TODO: estimate intercept
    phydev.github.io
"""

def predict(X, beta):
    """
        predict the regression
    """